The work [1]} combined CNN with metric learning-based HSIC framework which first utilizes CNN to extract deep spatial information using the first three PCs extracted by PCA. Then, in a metric learning-based framework, spectral and spatial features are fused together for spectral-spatial feature learning by embedding a metric learning regularization factor for classifier’s training (SVM). Similarly, [2]} combines multi-scale convolution-based CNN (MS-CNN) with diversified deep metrics based on determinantal point process (DPP) [3]} priors for (1-D spectral, 2-D spectral-spatial, and 3-D spectral-spatial) HSIC. Multiscale filters are used in CNN to obtain multi-scale features and DPP-based diversified metric transformation is performed to increase the inter-class variance and decrease intra-class variance, and better HSI representational ability. Final classification maps are obtained by using a softmax classifier.
